Title: Performance Evaluation of PCA and LDA for Face Recognition
Abstract: Automated face recognition has become a major field of interest. In this field several facial recognition algorithms have been explored in the past few decades. Progress has been made towards recognition under varying lighting conditions, poses and facial expressions. In a general context, a facial recognition algorithm and its implementation can be considered as a system. The input to the facial recognition system is a two dimensional image, while the system distinguishes the input image as a user's face from a pre-determined library of faces. Finally, the output is a discerned face image. This paper discusses different appearance based face recognition techniques. The experimentation includes the use of image preprocessing techniques followed by most popular dimensionality reduction algorithms based on PCA and LDA. Here our aim is to evaluate the performance of face recognition algorithms based on principle component analysis and linear discriminant analysis on small training data set. The result obtained showed that PCA outperforms LDA.
Publication Year: 2013
Publication Date: 2013-01-01
Language: en
Type: article
Access and Citation
Cited By Count: 6
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